55 citations · 63 across the 11 of their papers we have counts for
16 papers
NOMAI: Scalable Federated Learning with Private Weights
Yiyi Sun, Mushtari Sadia, Amrita Roy Chowdhury
The twin goals of privacy-preserving federated learning (FL) -- harnessing distributed data while protecting the parties who contribute it -- demands three properties: model privac…
PIMENTO: A Privacy Framework for Querying Text
Mushtari Sadia, Ang Chen, Amrita Roy Chowdhury
Currently, there are two state-of-the-art, complementary privacy guarantees: contextual integrity (CI) for what may flow, and differential privacy (DP) for what may be inferred. Ye…
Trinqet: Private Triangle and Quadrangle Counting over Distributed Graphs
Mushtari Sadia, Amrita Roy Chowdhury
Triangle and quadrangle counts are core statistics in graph analysis. Yet many real-world graphs are distributed across multiple parties and encode highly sensitive relationships,…
A Case for Computing on Unstructured Data
Mushtari Sadia, Amrita Roy Chowdhury, Ang Chen
Unstructured data, such as text, images, audio, and video, comprises the vast majority of the world's information, yet it remains poorly supported by traditional data systems that…
Piquant: Private Quantile Estimation in the Two-Server Model
Hannah Keller, Jacob Imola, Fabrizio Boninsegna +2
Quantiles are key in distributed analytics, but computing them over sensitive data risks privacy. Local differential privacy (LDP) offers strong protection but lower accuracy than…
What Really is a Member? Discrediting Membership Inference via Poisoning
Neal Mangaokar, Ashish Hooda, Zhuohang Li +5
Membership inference tests aim to determine whether a particular data point was included in a language model's training set. However, recent works have shown that such tests often…
SQUiD: Synthesizing Relational Databases from Unstructured Text
Mushtari Sadia, Zhenning Yang, Yunming Xiao +2
Relational databases are central to modern data management, yet most data exists in unstructured forms like text documents. To bridge this gap, we leverage large language models (L…
Differentially Private Quantiles with Smaller Error
Jacob Imola, Fabrizio Boninsegna, Hannah Keller +3
In the approximate quantiles problem, the goal is to output quantile estimates, the ranks of which are as close as possible to given quantiles $0 \leq q_1 \leq\dots \leq q_…
Prmpt: Sanitizing Sensitive Prompts for LLMs
Amrita Roy Chowdhury, David Glukhov, Divyam Anshumaan +4
The rise of large language models (LLMs) has introduced new privacy challenges, particularly during inference where sensitive information in prompts may be exposed to proprietary L…
Metric Differential Privacy at the User-Level Via the Earth Mover's Distance
Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri
Metric differential privacy (DP) provides heterogeneous privacy guarantees based on a distance between the pair of inputs. It is a widely popular notion of privacy since it capture…
FairProof : Confidential and Certifiable Fairness for Neural Networks
Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh +1
Machine learning models are increasingly used in societal applications, yet legal and privacy concerns demand that they very often be kept confidential. Consequently, there is a gr…
Identifying and Mitigating the Security Risks of Generative AI
Clark Barrett, Brad Boyd, Elie Burzstein +20
Every major technical invention resurfaces the dual-use dilemma -- the new technology has the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, s…
Can Membership Inferencing be Refuted?
Zhifeng Kong, Amrita Roy Chowdhury, Kamalika Chaudhuri
Membership inference (MI) attack is currently the most popular test for measuring privacy leakage in machine learning models. Given a machine learning model, a data point and some…
A Shuffling Framework for Local Differential Privacy
Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri +1
ldp deployments are vulnerable to inference attacks as an adversary can link the noisy responses to their identity and subsequently, auxiliary information using the order of the da…
ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks
Zhichuang Sun, Ruimin Sun, Changming Liu +3
With the increased usage of AI accelerators on mobile and edge devices, on-device machine learning (ML) is gaining popularity. Thousands of proprietary ML models are being deployed…
Crypt: Crypto-Assisted Differential Privacy on Untrusted Servers
Amrita Roy Chowdhury, Chenghong Wang, Xi He +2
Differential privacy (DP) has steadily become the de-facto standard for achieving privacy in data analysis, which is typically implemented either in the "central" or "local" model.…